Nonparametric mixture models for supervised image parcellation
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Golland-Nonparametric Mixture Models for Supervised.pdf
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Author(s)
Sabuncu, Mert R.
Yeo, B. T. Thomas
Van Leemput, Koen
Fischl, Bruce
Golland, Polina
Date Issued
September 2009
Journal
Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI)
Citation
MR Sabuncu, BTT Yeo, K Van Leemput, B Fischl, P Golland. Nonparametric Mixture Models for Supervised Image Parcellation. Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI) workshop on Probabilistic Models for Medical Image Analysis (PMMIA), pages 301-313, 2009.
Version
Original manuscript
Abstract
We present a nonparametric, probabilistic mixture model for the supervised parcellation of images.
The proposed model yields segmentation algorithms conceptually similar to the recently developed
label fusion methods, which register a new image with each training image separately. Segmentation
is achieved via the fusion of transferred manual labels. We show that in our framework various
settings of a model parameter yield algorithms that use image intensity information differently in
determining the weight of a training subject during fusion. One particular setting computes a single,
global weight per training subject, whereas another setting uses locally varying weights when fusing
the training data. The proposed nonparametric parcellation approach capitalizes on recently
developed fast and robust pairwise image alignment tools. The use of multiple registrations allows
the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric
brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and
subcortical structures. The results demonstrate that the proposed nonparametric segmentation
framework yields significantly better segmentation than state-of-the-art algorithms.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2930597/pdf/nihms-179665.pdf
